ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)

1 Setup

Instalar version en desarrollo.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")
library(emmeans)
library(corrplot)
library(multcomp)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd())
Project:  C:/Users/floza/git/prochira_maiz_morado
session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2024-08-07
 pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
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 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
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 [1] C:/Users/floza/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Refrencias

  • (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
  • (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
  • (HCPC) https://youtu.be/EJqYTDTJJug

3 Import data

https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741

url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"

gs <- url %>% 
  as_sheets_id()

imbibition <- gs %>% 
  range_read("imbibition") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(time = tiempo, .after = tiempo) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:tiempo, ~ as.factor(.))) 

str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tiempo     : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ time       : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
##  $ peso       : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...

germination <- gs %>% 
  range_read("germination") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(trat = case_when(
    tratamiento %in% "Agua Destilada" ~ "T0"
    , tratamiento %in% "Algas Marinas 1 L/cil" ~ "T1"
    , tratamiento %in% "Algas Marinas 1,5 L/cil" ~ "T2"
    , tratamiento %in% "Azufre 100 gr.200 L-1" ~ "T3"
    , tratamiento %in% "Azufre 150 gr.200 L-1" ~ "T4"
    , tratamiento %in% "Suero de leche 10%" ~ "T5"
    , tratamiento %in% "Suero de leche 30%" ~ "T6"
  ), .before = tratamiento) %>% 
  mutate(across(1:variedad, ~ as.factor(.))) 

str(germination)
## tibble [42 × 11] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dia 1      : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
##  $ dia 2      : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
##  $ dia 3      : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
##  $ total      : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
##  $ pg         : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
##  $ vg         : num [1:42] 2.67 3 3 2 1.5 ...
##  $ ig         : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...

plantula <- gs %>% 
  range_read("plantula") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:variedad, ~ as.factor(.)))

str(plantula)
## tibble [210 × 17] (S3: tbl_df/tbl/data.frame)
##  $ bloque         : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
##  $ trat           : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento    : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad       : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ raiz_lgtd      : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
##  $ gsr_raiz       : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
##  $ num_raiz       : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
##  $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
##  $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
##  $ alt_planta     : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
##  $ gsr_tallo      : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
##  $ nhp_hoja       : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
##  $ larg_hoja      : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
##  $ grs_hoja       : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
##  $ anch_hoja      : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
##  $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
##  $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...

4 Tratamientos

imbibition %>% 
  group_by(trat, tratamiento) %>% 
  summarise(n = n()) %>% 
  select(!n)
## # A tibble: 7 × 2
## # Groups:   trat [7]
##   trat  tratamiento            
##   <fct> <fct>                  
## 1 T0    Agua Destilada         
## 2 T1    Algas Marinas 1 L/cil  
## 3 T2    Algas Marinas 1,5 L/cil
## 4 T3    Azufre 100 gr.200 L-1  
## 5 T4    Azufre 150 gr.200 L-1  
## 6 T5    Suero de leche 10%     
## 7 T6    Suero de leche 30%

5 Data summary

sm <- imbibition %>% 
  group_by(tratamiento, variedad, tiempo) %>% 
  summarise(across(peso, ~ sum(!is.na(.))))

sm
## # A tibble: 70 × 4
## # Groups:   tratamiento, variedad [14]
##    tratamiento    variedad tiempo  peso
##    <fct>          <fct>    <fct>  <int>
##  1 Agua Destilada Creole   0         30
##  2 Agua Destilada Creole   3         30
##  3 Agua Destilada Creole   6         30
##  4 Agua Destilada Creole   9         30
##  5 Agua Destilada Creole   12        30
##  6 Agua Destilada Hybrid   0         30
##  7 Agua Destilada Hybrid   3         30
##  8 Agua Destilada Hybrid   6         30
##  9 Agua Destilada Hybrid   9         30
## 10 Agua Destilada Hybrid   12        30
## # ℹ 60 more rows

sm <- germination %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(pg:ig, ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 5
## # Groups:   tratamiento [7]
##    tratamiento             variedad    pg    vg    ig
##    <fct>                   <fct>    <int> <int> <int>
##  1 Agua Destilada          Creole       3     3     3
##  2 Agua Destilada          Hybrid       3     3     3
##  3 Algas Marinas 1 L/cil   Creole       3     3     3
##  4 Algas Marinas 1 L/cil   Hybrid       3     3     3
##  5 Algas Marinas 1,5 L/cil Creole       3     3     3
##  6 Algas Marinas 1,5 L/cil Hybrid       3     3     3
##  7 Azufre 100 gr.200 L-1   Creole       3     3     3
##  8 Azufre 100 gr.200 L-1   Hybrid       3     3     3
##  9 Azufre 150 gr.200 L-1   Creole       3     3     3
## 10 Azufre 150 gr.200 L-1   Hybrid       3     3     3
## 11 Suero de leche 10%      Creole       3     3     3
## 12 Suero de leche 10%      Hybrid       3     3     3
## 13 Suero de leche 30%      Creole       3     3     3
## 14 Suero de leche 30%      Hybrid       3     3     3

sm <- plantula %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(where(is.numeric), ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 15
## # Groups:   tratamiento [7]
##    tratamiento             variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
##    <fct>                   <fct>        <int>    <int>    <int>          <int>
##  1 Agua Destilada          Creole          15       15       15             15
##  2 Agua Destilada          Hybrid          15       15       15             15
##  3 Algas Marinas 1 L/cil   Creole          15       15       15             15
##  4 Algas Marinas 1 L/cil   Hybrid          15       15       15             15
##  5 Algas Marinas 1,5 L/cil Creole          15       15       15             15
##  6 Algas Marinas 1,5 L/cil Hybrid          15       15       15             15
##  7 Azufre 100 gr.200 L-1   Creole          15       15       15             15
##  8 Azufre 100 gr.200 L-1   Hybrid          15       15       15             15
##  9 Azufre 150 gr.200 L-1   Creole          15       15       15             15
## 10 Azufre 150 gr.200 L-1   Hybrid          15       15       15             15
## 11 Suero de leche 10%      Creole          15       15       15             15
## 12 Suero de leche 10%      Hybrid          15       15       15             15
## 13 Suero de leche 30%      Creole          15       15       15             15
## 14 Suero de leche 30%      Hybrid          15       15       15             15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## #   nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## #   peso_fres_brote <int>, peso_seco_brote <int>

6 Objetivos

  1. Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

  2. Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

6.1 Objetivo Específico 1

Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

  • Imbibiciación, % germinación, velocidad e IG

6.1.1 Imbibición

trait <- "peso"
fb <- imbibition

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad +  (1 + tiempo|tratamiento)") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tiempo +  trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad tiempo tratamiento peso resi res_MAD rawp.BHStud adjp bholm out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: peso
##                 Df  Sum Sq Mean Sq  F value              Pr(>F)    
## bloque           2  0.0021 0.00105   0.1222               0.885    
## tiempo           4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## trat             6  3.2174 0.53624  62.1186 <0.0000000000000002 ***
## variedad         1  0.6165 0.61649  71.4150 <0.0000000000000002 ***
## trat:variedad    6  2.6467 0.44111  51.0987 <0.0000000000000002 ***
## Residuals     2080 17.9556 0.00863                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ tiempo|variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
tiempo variedad trat emmean SE df lower.CL upper.CL group
1 12 Creole T0 0.8296986 0.0086019 2080 0.8128293 0.8465678 a
3 9 Creole T0 0.8279129 0.0086019 2080 0.8110436 0.8447821 a
2 3 Creole T0 0.7645081 0.0086019 2080 0.7476388 0.7813774 b
4 6 Creole T0 0.7620295 0.0086019 2080 0.7451603 0.7788988 b
5 0 Creole T0 0.6398176 0.0086019 2080 0.6229483 0.6566869 c
11 12 Creole T1 0.7157719 0.0086019 2080 0.6989026 0.7326412 a
13 9 Creole T1 0.7139862 0.0086019 2080 0.6971169 0.7308555 a
12 3 Creole T1 0.6505814 0.0086019 2080 0.6337122 0.6674507 b
14 6 Creole T1 0.6481029 0.0086019 2080 0.6312336 0.6649721 b
15 0 Creole T1 0.5258910 0.0086019 2080 0.5090217 0.5427602 c
21 12 Creole T2 0.6749719 0.0086019 2080 0.6581026 0.6918412 a
23 9 Creole T2 0.6731862 0.0086019 2080 0.6563169 0.6900555 a
22 3 Creole T2 0.6097814 0.0086019 2080 0.5929122 0.6266507 b
24 6 Creole T2 0.6073029 0.0086019 2080 0.5904336 0.6241721 b
25 0 Creole T2 0.4850910 0.0086019 2080 0.4682217 0.5019602 c
31 12 Creole T3 0.6591052 0.0086019 2080 0.6422360 0.6759745 a
33 9 Creole T3 0.6573195 0.0086019 2080 0.6404503 0.6741888 a
32 3 Creole T3 0.5939148 0.0086019 2080 0.5770455 0.6107840 b
34 6 Creole T3 0.5914362 0.0086019 2080 0.5745669 0.6083055 b
35 0 Creole T3 0.4692243 0.0086019 2080 0.4523550 0.4860936 c
41 12 Creole T4 0.6322386 0.0086019 2080 0.6153693 0.6491078 a
43 9 Creole T4 0.6304529 0.0086019 2080 0.6135836 0.6473221 a
42 3 Creole T4 0.5670481 0.0086019 2080 0.5501788 0.5839174 b
44 6 Creole T4 0.5645695 0.0086019 2080 0.5477003 0.5814388 b
45 0 Creole T4 0.4423576 0.0086019 2080 0.4254883 0.4592269 c
51 12 Creole T5 0.8092386 0.0086019 2080 0.7923693 0.8261078 a
53 9 Creole T5 0.8074529 0.0086019 2080 0.7905836 0.8243221 a
52 3 Creole T5 0.7440481 0.0086019 2080 0.7271788 0.7609174 b
54 6 Creole T5 0.7415695 0.0086019 2080 0.7247003 0.7584388 b
55 0 Creole T5 0.6193576 0.0086019 2080 0.6024883 0.6362269 c
61 12 Creole T6 0.7740386 0.0086019 2080 0.7571693 0.7909078 a
63 9 Creole T6 0.7722529 0.0086019 2080 0.7553836 0.7891221 a
62 3 Creole T6 0.7088481 0.0086019 2080 0.6919788 0.7257174 b
64 6 Creole T6 0.7063695 0.0086019 2080 0.6895003 0.7232388 b
65 0 Creole T6 0.5841576 0.0086019 2080 0.5672883 0.6010269 c
6 12 Hybrid T0 0.7764386 0.0086019 2080 0.7595693 0.7933078 a
8 9 Hybrid T0 0.7746529 0.0086019 2080 0.7577836 0.7915221 a
7 3 Hybrid T0 0.7112481 0.0086019 2080 0.6943788 0.7281174 b
9 6 Hybrid T0 0.7087695 0.0086019 2080 0.6919003 0.7256388 b
10 0 Hybrid T0 0.5865576 0.0086019 2080 0.5696883 0.6034269 c
16 12 Hybrid T1 0.7279719 0.0086019 2080 0.7111026 0.7448412 a
18 9 Hybrid T1 0.7261862 0.0086019 2080 0.7093169 0.7430555 a
17 3 Hybrid T1 0.6627814 0.0086019 2080 0.6459122 0.6796507 b
19 6 Hybrid T1 0.6603029 0.0086019 2080 0.6434336 0.6771721 b
20 0 Hybrid T1 0.5380910 0.0086019 2080 0.5212217 0.5549602 c
26 12 Hybrid T2 0.7881052 0.0086019 2080 0.7712360 0.8049745 a
28 9 Hybrid T2 0.7863195 0.0086019 2080 0.7694503 0.8031888 a
27 3 Hybrid T2 0.7229148 0.0086019 2080 0.7060455 0.7397840 b
29 6 Hybrid T2 0.7204362 0.0086019 2080 0.7035669 0.7373055 b
30 0 Hybrid T2 0.5982243 0.0086019 2080 0.5813550 0.6150936 c
36 12 Hybrid T3 0.7332386 0.0086019 2080 0.7163693 0.7501078 a
38 9 Hybrid T3 0.7314529 0.0086019 2080 0.7145836 0.7483221 a
37 3 Hybrid T3 0.6680481 0.0086019 2080 0.6511788 0.6849174 b
39 6 Hybrid T3 0.6655695 0.0086019 2080 0.6487003 0.6824388 b
40 0 Hybrid T3 0.5433576 0.0086019 2080 0.5264883 0.5602269 c
46 12 Hybrid T4 0.7735052 0.0086019 2080 0.7566360 0.7903745 a
48 9 Hybrid T4 0.7717195 0.0086019 2080 0.7548503 0.7885888 a
47 3 Hybrid T4 0.7083148 0.0086019 2080 0.6914455 0.7251840 b
49 6 Hybrid T4 0.7058362 0.0086019 2080 0.6889669 0.7227055 b
50 0 Hybrid T4 0.5836243 0.0086019 2080 0.5667550 0.6004936 c
56 12 Hybrid T5 0.7615719 0.0086019 2080 0.7447026 0.7784412 a
58 9 Hybrid T5 0.7597862 0.0086019 2080 0.7429169 0.7766555 a
57 3 Hybrid T5 0.6963814 0.0086019 2080 0.6795122 0.7132507 b
59 6 Hybrid T5 0.6939029 0.0086019 2080 0.6770336 0.7107721 b
60 0 Hybrid T5 0.5716910 0.0086019 2080 0.5548217 0.5885602 c
66 12 Hybrid T6 0.7741052 0.0086019 2080 0.7572360 0.7909745 a
68 9 Hybrid T6 0.7723195 0.0086019 2080 0.7554503 0.7891888 a
67 3 Hybrid T6 0.7089148 0.0086019 2080 0.6920455 0.7257840 b
69 6 Hybrid T6 0.7064362 0.0086019 2080 0.6895669 0.7233055 b
70 0 Hybrid T6 0.5842243 0.0086019 2080 0.5673550 0.6010936 c

p1a <- mc %>% 
  plot_smr(type = "line"
           , x = "tiempo"
           , y = "emmean"
           , group = "trat"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Seed weight (g)"
           , xlab = "Time (h)"
           , glab = "Treatment"
           , ylimits = c(0.4, 1, 0.2)
           ) + 
  facet_wrap(. ~ variedad, ncol = 2) +
  theme(legend.position = "top") +
  guides(colour = guide_legend(nrow = 1))

p1a

6.1.2 Porcentaje de Germination

trait <- "pg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad pg resi res_MAD rawp.BHStud adjp bholm out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: pg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2   633.3   316.7  0.6222 0.544582   
## trat           6  7000.0  1166.7  2.2922 0.065673 . 
## variedad       1  4609.5  4609.5  9.0565 0.005753 **
## trat:variedad  6  6857.1  1142.9  2.2454 0.070466 . 
## Residuals     26 13233.3   509.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad trat emmean SE df lower.CL upper.CL group
2 Creole T0 86.66667 13.02529 26 59.8928044 113.44053 a
1 Hybrid T0 63.33333 13.02529 26 36.5594710 90.10720 a
3 Hybrid T1 70.00000 13.02529 26 43.2261377 96.77386 a
4 Creole T1 30.00000 13.02529 26 3.2261377 56.77386 b
5 Hybrid T2 56.66667 13.02529 26 29.8928044 83.44053 a
6 Creole T2 36.66667 13.02529 26 9.8928044 63.44053 a
7 Hybrid T3 66.66667 13.02529 26 39.8928044 93.44053 a
8 Creole T3 16.66667 13.02529 26 -10.1071956 43.44053 b
9 Hybrid T4 76.66667 13.02529 26 49.8928044 103.44053 a
10 Creole T4 26.66667 13.02529 26 -0.1071956 53.44053 b
11 Hybrid T5 70.00000 13.02529 26 43.2261377 96.77386 a
12 Creole T5 70.00000 13.02529 26 43.2261377 96.77386 a
13 Hybrid T6 43.33333 13.02529 26 16.5594710 70.10720 a
14 Creole T6 33.33333 13.02529 26 6.5594710 60.10720 a

p1b <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination ('%')"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 120, 20)
           ) 

p1b

6.1.3 Velocidad de germinación

trait <- "vg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad vg resi res_MAD rawp.BHStud adjp bholm out_flag
7 7 1 T2 Creole 1 -1.666667 -3.372454 0.0007450 0.0007450159 0.0305456 OUTLIER
34 34 1 T4 Hybrid 5 1.888889 3.822114 0.0001323 0.0001323123 0.0055571 OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: vg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  1.3321 0.66607  1.3664 0.274150   
## trat           6  8.6594 1.44323  2.9608 0.026214 * 
## variedad       1  2.0003 2.00025  4.1035 0.054051 . 
## trat:variedad  6 11.5622 1.92703  3.9533 0.006872 **
## Residuals     24 11.6989 0.48745                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad trat emmean SE df lower.CL upper.CL group
2 Creole T0 2.888889 0.4030931 24 2.0569455 3.720832 a
1 Hybrid T0 2.333333 0.4030931 24 1.5013900 3.165277 a
3 Hybrid T1 3.277778 0.4030931 24 2.4458344 4.109721 a
4 Creole T1 1.500000 0.4030931 24 0.6680567 2.331943 b
6 Creole T2 3.379630 0.5004960 24 2.3466566 4.412603 a
5 Hybrid T2 2.833333 0.4030931 24 2.0013900 3.665277 a
7 Hybrid T3 3.833333 0.4030931 24 3.0013900 4.665277 a
8 Creole T3 1.666667 0.4030931 24 0.8347233 2.498610 b
9 Hybrid T4 2.046296 0.5004960 24 1.0133232 3.079269 a
10 Creole T4 1.333333 0.4030931 24 0.5013900 2.165277 a
12 Creole T5 3.055556 0.4030931 24 2.2236122 3.887499 a
11 Hybrid T5 3.000000 0.4030931 24 2.1680567 3.831943 a
14 Creole T6 2.333333 0.4030931 24 1.5013900 3.165277 a
13 Hybrid T6 1.833333 0.4030931 24 1.0013900 2.665277 a

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination speed (days)"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 6, 1)
           ) 

p1c

6.1.4 Indice de germinación

trait <- "ig"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad ig resi res_MAD rawp.BHStud adjp bholm out_flag
25 25 1 T1 Hybrid 0.2 -1.466667 -3.29751 0.0009755 0.0009754607 0.0409693 OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ig
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  0.3050  0.1525  0.4149 0.664896   
## trat           6 10.3540  1.7257  4.6949 0.002507 **
## variedad       1  3.8850  3.8850 10.5697 0.003278 **
## trat:variedad  6  6.5489  1.0915  2.9695 0.024965 * 
## Residuals     25  9.1890  0.3676                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad trat emmean SE df lower.CL upper.CL group
2 Creole T0 2.6000000 0.3500293 25 1.8791012 3.3208988 a
1 Hybrid T0 1.7666667 0.3500293 25 1.0457678 2.4875655 a
3 Hybrid T1 2.3679487 0.4341579 25 1.4737838 3.2621137 a
4 Creole T1 0.6000000 0.3500293 25 -0.1208988 1.3208988 b
5 Hybrid T2 1.1333333 0.3500293 25 0.4124345 1.8542322 a
6 Creole T2 0.5666667 0.3500293 25 -0.1542322 1.2875655 a
7 Hybrid T3 1.2333333 0.3500293 25 0.5124345 1.9542322 a
8 Creole T3 0.1666667 0.3500293 25 -0.5542322 0.8875655 b
9 Hybrid T4 1.9666667 0.3500293 25 1.2457678 2.6875655 a
10 Creole T4 0.5333333 0.3500293 25 -0.1875655 1.2542322 b
11 Hybrid T5 1.7000000 0.3500293 25 0.9791012 2.4208988 a
12 Creole T5 1.6666667 0.3500293 25 0.9457678 2.3875655 a
13 Hybrid T6 1.0666667 0.3500293 25 0.3457678 1.7875655 a
14 Creole T6 0.5333333 0.3500293 25 -0.1875655 1.2542322 a

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination Index"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 5, 1)
           ) 

p1d

6.2 Figura 1

legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)

p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 1
            , labels = c("b", "c", "d")
            ) 

p1il <- list(legend, p1i) %>% 
  plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))


plot <- list(p1a, p1il) %>% 
  plot_grid(plotlist = .
            , ncol = 1
            , labels = c("a")
            , rel_heights = c(0.6, 1)
            )  
  
plot %>% 
  ggsave2(plot = ., "files/Fig-1.jpg"
         , units = "cm"
         , width = 24
         , height = 29
         )

plot %>% 
  ggsave2(plot = ., "files/Fig-1.eps"
         , units = "cm"
         , width = 24
         , height = 29
         )

knitr::include_graphics("files/Fig-1.jpg")

6.3 Objetivo Específico 2

Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

fb <- plantula %>% 
  select(!contains("fres"))

rsl <- 5:length(fb) %>% map(\(x) {
  
trait <- names(fb)[x]

cat("\n### ", trait)

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

cat("\n#### ",  "Diagnostico")

rmout$diagplot %>% print()

cat("\n#### ", "Outliers")

rmout$outliers  %>% kable() %>% print()

cat("\n#### ", "ANOVA")

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model) %>% anova_table %>% kable() %>% print()

cat("\n#### ", "Mean comparison")

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group") %>% 
  rename({{trait}} := "emmean")

mc %>% kable() %>% print()

plot <- mc %>% 
  plot_smr(x = "trat"
           , y = trait
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Treatments"
           , glab = "Variety"
           )

plot

list(mc = mc, plot = plot)
  
})

6.3.1 raiz_lgtd

6.3.1.1 Diagnostico

6.3.1.2 Outliers

index bloque trat variedad raiz_lgtd resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.1.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 7.20952380952369 3.60476190476185 0.419160753734702 0.658192586259791 ns
trat 6 113.028571428572 18.8380952380953 2.19048869455021 0.0455341138068884 *
variedad 1 2.30476190476197 2.30476190476197 0.267997100142146 0.605268321276777 ns
trat:variedad 6 240.761904761906 40.1269841269843 4.66595502175847 0.000186149204512116 ***
Residuals 194 1668.39047619048 8.59995090819833
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.1.4 Mean comparison

variedad trat raiz_lgtd SE df lower.CL upper.CL group
1 Hybrid T0 16.00000 0.7571856 194 14.506627 17.49337 a
2 Creole T0 11.00000 0.7571856 194 9.506627 12.49337 b
4 Creole T1 14.00000 0.7571856 194 12.506627 15.49337 a
3 Hybrid T1 13.93333 0.7571856 194 12.439961 15.42671 a
5 Hybrid T2 13.60000 0.7571856 194 12.106627 15.09337 a
6 Creole T2 13.33333 0.7571856 194 11.839961 14.82671 a
8 Creole T3 12.93333 0.7571856 194 11.439961 14.42671 a
7 Hybrid T3 12.46667 0.7571856 194 10.973294 13.96004 a
10 Creole T4 15.13333 0.7571856 194 13.639961 16.62671 a
9 Hybrid T4 14.00000 0.7571856 194 12.506627 15.49337 a
12 Creole T5 15.80000 0.7571856 194 14.306627 17.29337 a
11 Hybrid T5 13.40000 0.7571856 194 11.906627 14.89337 b
13 Hybrid T6 15.06667 0.7571856 194 13.573294 16.56004 a
14 Creole T6 14.80000 0.7571856 194 13.306627 16.29337 a

6.3.2 gsr_raiz

6.3.2.1 Diagnostico

6.3.2.2 Outliers

index bloque trat variedad gsr_raiz resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.2.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 0.041745714285714 0.020872857142857 0.527763692800836 0.590767266932123 ns
trat 6 2.85575333333334 0.47595888888889 12.0344722862891 0.0000000000176542819668879 ***
variedad 1 0.327257619047619 0.327257619047619 8.27460698569895 0.00447016897110515 **
trat:variedad 6 0.701705714285714 0.116950952380952 2.95706841103296 0.00874560396951841 **
Residuals 194 7.67262761904762 0.0395496269023073
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.2.4 Mean comparison

variedad trat gsr_raiz SE df lower.CL upper.CL group
2 Creole T0 1.2560000 0.0513482 194 1.1547275 1.3572725 a
1 Hybrid T0 1.0853333 0.0513482 194 0.9840609 1.1866058 b
3 Hybrid T1 0.8926667 0.0513482 194 0.7913942 0.9939391 a
4 Creole T1 0.7486667 0.0513482 194 0.6473942 0.8499391 b
5 Hybrid T2 0.9500000 0.0513482 194 0.8487275 1.0512725 a
6 Creole T2 0.9260000 0.0513482 194 0.8247275 1.0272725 a
8 Creole T3 0.8600000 0.0513482 194 0.7587275 0.9612725 a
7 Hybrid T3 0.7760000 0.0513482 194 0.6747275 0.8772725 a
10 Creole T4 0.9846667 0.0513482 194 0.8833942 1.0859391 a
9 Hybrid T4 0.7840000 0.0513482 194 0.6827275 0.8852725 b
12 Creole T5 1.1066667 0.0513482 194 1.0053942 1.2079391 a
11 Hybrid T5 0.9280000 0.0513482 194 0.8267275 1.0292725 b
14 Creole T6 1.0513333 0.0513482 194 0.9500609 1.1526058 a
13 Hybrid T6 0.9646667 0.0513482 194 0.8633942 1.0659391 a

6.3.3 num_raiz

6.3.3.1 Diagnostico

6.3.3.2 Outliers

index bloque trat variedad num_raiz resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.3.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 16.8285714285716 8.4142857142858 1.07887001240016 0.342010330536742 ns
trat 6 457.161904761905 76.1936507936509 9.7694620515435 0.0000000021095513439047 ***
variedad 1 4.28571428571414 4.28571428571414 0.549509344176629 0.459414622986879 ns
trat:variedad 6 153.714285714286 25.6190476190477 3.28484474630041 0.00422788227377547 **
Residuals 194 1513.0380952381 7.79916543937164
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.3.4 Mean comparison

variedad trat num_raiz SE df lower.CL upper.CL group
1 Hybrid T0 11.33333 0.7210717 194 9.911187 12.75548 a
2 Creole T0 10.86667 0.7210717 194 9.444520 12.28881 a
3 Hybrid T1 15.73333 0.7210717 194 14.311187 17.15548 a
4 Creole T1 14.53333 0.7210717 194 13.111187 15.95548 a
6 Creole T2 15.06667 0.7210717 194 13.644520 16.48881 a
5 Hybrid T2 12.00000 0.7210717 194 10.577854 13.42215 b
7 Hybrid T3 12.53333 0.7210717 194 11.111187 13.95548 a
8 Creole T3 10.66667 0.7210717 194 9.244520 12.08881 a
10 Creole T4 13.40000 0.7210717 194 11.977854 14.82215 a
9 Hybrid T4 10.93333 0.7210717 194 9.511187 12.35548 b
11 Hybrid T5 11.80000 0.7210717 194 10.377854 13.22215 a
12 Creole T5 11.33333 0.7210717 194 9.911187 12.75548 a
14 Creole T6 10.73333 0.7210717 194 9.311187 12.15548 a
13 Hybrid T6 10.26667 0.7210717 194 8.844520 11.68881 a

6.3.4 peso_seco_raiz

6.3.4.1 Diagnostico

6.3.4.2 Outliers

index bloque trat variedad peso_seco_raiz resi res_MAD rawp.BHStud adjp bholm out_flag
124 124 1 T1 Hybrid 2.60 1.300138 4.664528 0.0000031 0.00000309326204 0.0006465 OUTLIER
153 153 1 T3 Hybrid 2.88 1.497472 5.372504 0.0000001 0.00000007765065 0.0000163 OUTLIER
193 193 3 T5 Hybrid 1.94 1.218563 4.371860 0.0000123 0.00001231926601 0.0025624 OUTLIER

6.3.4.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 0.708888244834166 0.354444122417083 3.68543486170346 0.0268873108796916 *
trat 6 8.94085063640856 1.49014177273476 15.4941783225705 0.0000000000000187491544735756 ***
variedad 1 0.203977311319524 0.203977311319524 2.12091285082425 0.146941784139003 ns
trat:variedad 6 0.268201941388118 0.0447003235646863 0.46478449034871 0.833811880880163 ns
Residuals 191 18.3692915278854 0.0961743011931172
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.4.4 Mean comparison

variedad trat peso_seco_raiz SE df lower.CL upper.CL group
1 Hybrid T0 0.6646667 0.0800726 191 0.5067265 0.8226068 a
2 Creole T0 0.6346667 0.0800726 191 0.4767265 0.7926068 a
4 Creole T1 1.2140000 0.0800726 191 1.0560599 1.3719401 a
3 Hybrid T1 1.1876781 0.0829119 191 1.0241376 1.3512186 a
5 Hybrid T2 0.8340000 0.0800726 191 0.6760599 0.9919401 a
6 Creole T2 0.8133333 0.0800726 191 0.6553932 0.9712735 a
7 Hybrid T3 1.2562495 0.0829119 191 1.0927090 1.4197900 a
8 Creole T3 1.0566667 0.0800726 191 0.8987265 1.2146068 a
9 Hybrid T4 0.9240000 0.0800726 191 0.7660599 1.0819401 a
10 Creole T4 0.8433333 0.0800726 191 0.6853932 1.0012735 a
11 Hybrid T5 0.6809488 0.0829116 191 0.5174088 0.8444889 a
12 Creole T5 0.5526667 0.0800726 191 0.3947265 0.7106068 a
13 Hybrid T6 0.9426667 0.0800726 191 0.7847265 1.1006068 a
14 Creole T6 0.9320000 0.0800726 191 0.7740599 1.0899401 a

6.3.5 alt_planta

6.3.5.1 Diagnostico

6.3.5.2 Outliers

index bloque trat variedad alt_planta resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.5.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 135.895238095238 67.947619047619 2.57285007649253 0.0789200270213045 ns
trat 6 8203.78095238094 1367.29682539682 51.7729655743322 0.0000000000000000000000000000000000000994591853425956 ***
variedad 1 20.742857142857 20.742857142857 0.785432401233541 0.376582109302841 ns
trat:variedad 6 1204.52380952381 200.753968253968 7.60158883884396 0.000000244031006032972 ***
Residuals 194 5123.43809523809 26.4094747177221
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.5.4 Mean comparison

variedad trat alt_planta SE df lower.CL upper.CL group
2 Creole T0 30.80000 1.326888 194 28.18302 33.41698 a
1 Hybrid T0 26.13333 1.326888 194 23.51636 28.75031 b
3 Hybrid T1 46.33333 1.326888 194 43.71636 48.95031 a
4 Creole T1 40.26667 1.326888 194 37.64969 42.88364 b
5 Hybrid T2 44.20000 1.326888 194 41.58302 46.81698 a
6 Creole T2 39.40000 1.326888 194 36.78302 42.01698 b
8 Creole T3 38.26667 1.326888 194 35.64969 40.88364 a
7 Hybrid T3 37.06667 1.326888 194 34.44969 39.68364 a
10 Creole T4 40.53333 1.326888 194 37.91636 43.15031 a
9 Hybrid T4 33.06667 1.326888 194 30.44969 35.68364 b
11 Hybrid T5 30.26667 1.326888 194 27.64969 32.88364 a
12 Creole T5 27.53333 1.326888 194 24.91636 30.15031 a
13 Hybrid T6 28.80000 1.326888 194 26.18302 31.41698 a
14 Creole T6 24.66667 1.326888 194 22.04969 27.28364 b

6.3.6 gsr_tallo

6.3.6.1 Diagnostico

6.3.6.2 Outliers

index bloque trat variedad gsr_tallo resi res_MAD rawp.BHStud adjp bholm out_flag
139 139 1 T2 Hybrid 3.04 -2.328344 -4.180425 0.0000291 0.0000290965 0.0061103 OUTLIER

6.3.6.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 5.39985048550228 2.69992524275114 7.27885665755039 0.000896145755015216 ***
trat 6 36.0290245706251 6.00483742843752 16.1887263400496 0.00000000000000461731449963955 ***
variedad 1 1.79298499200426 1.79298499200426 4.8337933729749 0.0290959732837517 *
trat:variedad 6 6.51630589531172 1.08605098255195 2.92793641083642 0.00933526126495123 **
Residuals 193 71.5889316642121 0.370927107068456
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.6.4 Mean comparison

variedad trat gsr_tallo SE df lower.CL upper.CL group
2 Creole T0 4.471333 0.1572529 193 4.161179 4.781488 a
1 Hybrid T0 3.890667 0.1572529 193 3.580512 4.200821 b
3 Hybrid T1 4.917333 0.1572529 193 4.607178 5.227488 a
4 Creole T1 4.849333 0.1572529 193 4.539179 5.159488 a
5 Hybrid T2 5.378330 0.1628281 193 5.057179 5.699481 a
6 Creole T2 5.032000 0.1572529 193 4.721845 5.342155 a
8 Creole T3 4.509333 0.1572529 193 4.199179 4.819488 a
7 Hybrid T3 4.016000 0.1572529 193 3.705845 4.326155 b
10 Creole T4 4.316667 0.1572529 193 4.006512 4.626822 a
9 Hybrid T4 3.704000 0.1572529 193 3.393845 4.014155 b
12 Creole T5 4.195333 0.1572529 193 3.885178 4.505488 a
11 Hybrid T5 4.066667 0.1572529 193 3.756512 4.376822 a
13 Hybrid T6 4.218000 0.1572529 193 3.907845 4.528155 a
14 Creole T6 4.095333 0.1572529 193 3.785178 4.405488 a

6.3.7 nhp_hoja

6.3.7.1 Diagnostico

6.3.7.2 Outliers

index bloque trat variedad nhp_hoja resi res_MAD rawp.BHStud adjp bholm out_flag
4 4 1 T0 Creole 6 0.9558691 4.835436 0.0000013 0.0000013285389 0.0002763 OUTLIER
5 5 1 T0 Creole 4 -1.0441309 -5.281925 0.0000001 0.0000001278335 0.0000268 OUTLIER
51 51 2 T3 Creole 6 0.8721830 4.412096 0.0000102 0.0000102374945 0.0020987 OUTLIER
57 57 3 T3 Creole 6 0.9052812 4.579529 0.0000047 0.0000046602472 0.0009647 OUTLIER
66 66 2 T4 Creole 6 0.7388497 3.737605 0.0001858 0.0001857817174 0.0369706 OUTLIER
72 72 3 T4 Creole 6 0.7719479 3.905038 0.0000942 0.0000942105812 0.0188421 OUTLIER
88 88 3 T5 Creole 6 0.9719479 4.916774 0.0000009 0.0000008798196 0.0001839 OUTLIER
152 152 1 T3 Hybrid 6 0.8225357 4.160946 0.0000317 0.0000316932621 0.0064337 OUTLIER
155 155 1 T3 Hybrid 6 0.8225357 4.160946 0.0000317 0.0000316932621 0.0064337 OUTLIER
166 166 1 T4 Hybrid 6 0.8225357 4.160946 0.0000317 0.0000316932621 0.0064337 OUTLIER
173 173 2 T4 Hybrid 6 0.8721830 4.412096 0.0000102 0.0000102374945 0.0020987 OUTLIER
181 181 1 T5 Hybrid 6 0.8892024 4.498191 0.0000069 0.0000068534130 0.0014118 OUTLIER

6.3.7.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 0.684993592642831 0.342496796321415 3.22074419272831 0.0422116625843577 *
trat 6 13.0076855867699 2.16794759779498 20.3867735719924 0.00000000000000000344316718685895 ***
variedad 1 0.271082180181899 0.271082180181899 2.54918109293389 0.112086289536158 ns
trat:variedad 6 0.722601130029324 0.120433521671554 1.13252319350008 0.345154455799285 ns
Residuals 182 19.3540415507802 0.106340887641649
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.7.4 Mean comparison

variedad trat nhp_hoja SE df lower.CL upper.CL group
2 Creole T0 5.010904 0.0905876 182 4.832167 5.189641 a
1 Hybrid T0 4.666667 0.0841985 182 4.500536 4.832797 b
4 Creole T1 5.533333 0.0841985 182 5.367203 5.699464 a
3 Hybrid T1 5.466667 0.0841985 182 5.300536 5.632797 a
5 Hybrid T2 5.533333 0.0841985 182 5.367203 5.699464 a
6 Creole T2 5.533333 0.0841985 182 5.367203 5.699464 a
7 Hybrid T3 5.010904 0.0905876 182 4.832167 5.189641 a
8 Creole T3 4.994548 0.0904797 182 4.816024 5.173072 a
10 Creole T4 5.148394 0.0904797 182 4.969870 5.326918 a
9 Hybrid T4 5.005028 0.0904787 182 4.826506 5.183550 a
11 Hybrid T5 5.005063 0.0871860 182 4.833037 5.177088 a
12 Creole T5 4.995331 0.0871851 182 4.823308 5.167355 a
13 Hybrid T6 5.000000 0.0841985 182 4.833869 5.166131 a
14 Creole T6 5.000000 0.0841985 182 4.833869 5.166131 a

6.3.8 larg_hoja

6.3.8.1 Diagnostico

6.3.8.2 Outliers

index bloque trat variedad larg_hoja resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.8.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 24.2666666666667 12.1333333333333 0.91282316442606 0.403106357049745 ns
trat 6 3517.25714285715 586.209523809525 44.1021125720196 0.000000000000000000000000000000000935746697641098 ***
variedad 1 12.8761904761905 12.8761904761905 0.968710296941942 0.326227923148263 ns
trat:variedad 6 830.857142857142 138.47619047619 10.4179347023194 0.000000000526043977707136 ***
Residuals 194 2578.66666666667 13.2920962199313
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.8.4 Mean comparison

variedad trat larg_hoja SE df lower.CL upper.CL group
2 Creole T0 26.20000 0.94135 194 24.34341 28.05659 a
1 Hybrid T0 22.66667 0.94135 194 20.81007 24.52326 b
3 Hybrid T1 36.26667 0.94135 194 34.41007 38.12326 a
4 Creole T1 28.33333 0.94135 194 26.47674 30.18993 b
6 Creole T2 34.80000 0.94135 194 32.94341 36.65659 a
5 Hybrid T2 33.86667 0.94135 194 32.01007 35.72326 a
7 Hybrid T3 28.93333 0.94135 194 27.07674 30.78993 a
8 Creole T3 28.60000 0.94135 194 26.74341 30.45659 a
10 Creole T4 28.26667 0.94135 194 26.41007 30.12326 a
9 Hybrid T4 23.33333 0.94135 194 21.47674 25.18993 b
11 Hybrid T5 26.46667 0.94135 194 24.61007 28.32326 a
12 Creole T5 23.40000 0.94135 194 21.54341 25.25659 b
13 Hybrid T6 23.06667 0.94135 194 21.21007 24.92326 a
14 Creole T6 21.53333 0.94135 194 19.67674 23.38993 a

6.3.9 grs_hoja

6.3.9.1 Diagnostico

6.3.9.2 Outliers

index bloque trat variedad grs_hoja resi res_MAD rawp.BHStud adjp bholm out_flag
193 193 3 T5 Hybrid 1.43 0.5793333 3.757259 0.0001718 0.0001717844 0.0359029 OUTLIER
197 197 1 T6 Hybrid 1.45 0.6780000 4.397161 0.0000110 0.0000109676 0.0023032 OUTLIER

6.3.9.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 0.0676350949593889 0.0338175474796944 1.18553751980215 0.307808659010028 ns
trat 6 1.87309077321147 0.312181795535245 10.9441180448838 0.00000000017837157586309 ***
variedad 1 0.000660363330975994 0.000660363330975994 0.0231502744556993 0.879226703591861 ns
trat:variedad 6 0.634360294681688 0.105726715780281 3.70644821237387 0.00164817394159611 **
Residuals 192 5.47681453150879 0.0285250756849416
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.9.4 Mean comparison

variedad trat grs_hoja SE df lower.CL upper.CL group
2 Creole T0 0.9700000 0.0436082 192 0.8839874 1.0560126 a
1 Hybrid T0 0.8846667 0.0436082 192 0.7986541 0.9706793 a
3 Hybrid T1 0.8446667 0.0436082 192 0.7586541 0.9306793 a
4 Creole T1 0.6346667 0.0436082 192 0.5486541 0.7206793 b
5 Hybrid T2 0.7553333 0.0436082 192 0.6693207 0.8413459 a
6 Creole T2 0.7006667 0.0436082 192 0.6146541 0.7866793 a
7 Hybrid T3 0.6880000 0.0436082 192 0.6019874 0.7740126 a
8 Creole T3 0.6300000 0.0436082 192 0.5439874 0.7160126 a
10 Creole T4 0.6853333 0.0436082 192 0.5993207 0.7713459 a
9 Hybrid T4 0.6366667 0.0436082 192 0.5506541 0.7226793 a
12 Creole T5 0.9406667 0.0436082 192 0.8546541 1.0266793 a
11 Hybrid T5 0.8075459 0.0451543 192 0.7184838 0.8966081 b
14 Creole T6 0.8126667 0.0436082 192 0.7266541 0.8986793 a
13 Hybrid T6 0.7247584 0.0451543 192 0.6356962 0.8138205 a

6.3.10 anch_hoja

6.3.10.1 Diagnostico

6.3.10.2 Outliers

index bloque trat variedad anch_hoja resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.10.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 15.7079895238095 7.85399476190473 1.13598726704289 0.323231382586704 ns
trat 6 291.914404761905 48.6524007936508 7.0369932102235 0.000000862224352741745 ***
variedad 1 0.350554285714286 0.350554285714286 0.0507035231179815 0.822080596652288 ns
trat:variedad 6 139.877105714286 23.3128509523809 3.3719276168642 0.00348053301722631 **
Residuals 194 1341.27822380952 6.91380527736868
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.10.4 Mean comparison

variedad trat anch_hoja SE df lower.CL upper.CL group
2 Creole T0 18.86800 0.6789112 194 17.52901 20.20699 a
1 Hybrid T0 16.61133 0.6789112 194 15.27234 17.95033 b
3 Hybrid T1 20.04333 0.6789112 194 18.70434 21.38233 a
4 Creole T1 18.95867 0.6789112 194 17.61967 20.29766 a
5 Hybrid T2 21.76933 0.6789112 194 20.43034 23.10833 a
6 Creole T2 19.03467 0.6789112 194 17.69567 20.37366 b
8 Creole T3 19.05067 0.6789112 194 17.71167 20.38966 a
7 Hybrid T3 17.51800 0.6789112 194 16.17901 18.85699 a
10 Creole T4 17.06933 0.6789112 194 15.73034 18.40833 a
9 Hybrid T4 16.07400 0.6789112 194 14.73501 17.41299 a
11 Hybrid T5 19.51800 0.6789112 194 18.17901 20.85699 a
12 Creole T5 18.28667 0.6789112 194 16.94767 19.62566 a
13 Hybrid T6 17.86067 0.6789112 194 16.52167 19.19966 a
14 Creole T6 17.55467 0.6789112 194 16.21567 18.89366 a

6.3.11 peso_seco_brote

6.3.11.1 Diagnostico

6.3.11.2 Outliers

index bloque trat variedad peso_seco_brote resi res_MAD rawp.BHStud adjp bholm out_flag
42 42 3 T2 Creole 2.39 1.216944 3.686305 0.0002275 0.00022753340922 0.0464168 OUTLIER
72 72 3 T4 Creole 3.09 1.776277 5.380610 0.0000001 0.00000007423391 0.0000156 OUTLIER
127 127 2 T1 Hybrid 3.27 1.233120 3.735306 0.0001875 0.00018748728839 0.0384349 OUTLIER
134 134 3 T1 Hybrid 0.73 -1.285056 -3.892628 0.0000992 0.00009916401076 0.0204278 OUTLIER
169 169 1 T4 Hybrid 2.85 1.391936 4.216384 0.0000248 0.00002482511247 0.0051388 OUTLIER
170 170 1 T4 Hybrid 2.93 1.471936 4.458716 0.0000082 0.00000824521585 0.0017150 OUTLIER
172 172 2 T4 Hybrid 2.98 1.503120 4.553176 0.0000053 0.00000528419693 0.0011044 OUTLIER

6.3.11.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
bloque 2 0.502411537322084 0.251205768661042 1.46529227890307 0.233650440367885 ns
trat 6 43.010518479078 7.168419746513 41.8136500705716 0.0000000000000000000000000000000424415542003033 ***
variedad 1 0.0341827573920979 0.0341827573920979 0.199389252664187 0.655730939528593 ns
trat:variedad 6 3.43432182335683 0.572386970559472 3.33875377534505 0.00378990456664722 **
Residuals 187 32.0587772255111 0.17143731136637
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.11.4 Mean comparison

variedad trat peso_seco_brote SE df lower.CL upper.CL group
2 Creole T0 0.7100000 0.1069072 187 0.4991008 0.9208992 a
1 Hybrid T0 0.4253333 0.1069072 187 0.2144341 0.6362325 a
3 Hybrid T1 2.0300403 0.1148803 187 1.8034124 2.2566683 a
4 Creole T1 1.5046667 0.1069072 187 1.2937675 1.7155659 b
5 Hybrid T2 1.2113333 0.1069072 187 1.0004341 1.4222325 a
6 Creole T2 1.0926154 0.1106987 187 0.8742365 1.3109942 a
8 Creole T3 1.7186667 0.1069072 187 1.5077675 1.9295659 a
7 Hybrid T3 1.4093333 0.1069072 187 1.1984341 1.6202325 b
10 Creole T4 1.1933296 0.1106987 187 0.9749508 1.4117085 a
9 Hybrid T4 1.0985717 0.1196740 187 0.8624872 1.3346563 a
12 Creole T5 0.6500000 0.1069072 187 0.4391008 0.8608992 a
11 Hybrid T5 0.6373333 0.1069072 187 0.4264341 0.8482325 a
14 Creole T6 0.5486667 0.1069072 187 0.3377675 0.7595659 a
13 Hybrid T6 0.4586667 0.1069072 187 0.2477675 0.6695659 a

6.3.12 Figure 2


legend <- cowplot::get_plot_component(rsl[[1]]$plot, 'guide-box-top', return_all = TRUE)

fig <- list(
  rsl[[1]]$plot + labs(x = NULL, y = "Root length (cm)") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 20), n.breaks = 5) +
    theme(legend.position="none"
          , axis.title.x=element_blank()
          , axis.text.x=element_blank()
          , axis.ticks.x=element_blank())
  
  , rsl[[2]]$plot + labs(x = NULL, y = "Root thickness (mm)") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 2), n.breaks = 5) +
    theme(legend.position="none"
          , axis.title.x=element_blank()
          , axis.text.x=element_blank()
          , axis.ticks.x=element_blank())
  
  , rsl[[3]]$plot  + labs(x = NULL, y = "Root number") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 20), n.breaks = 5) +
    theme(legend.position="none"
          , axis.title.x=element_blank()
          , axis.text.x=element_blank()
          , axis.ticks.x=element_blank())
  
  , rsl[[4]]$plot + labs(y = "Root dry weight (g)") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 2), n.breaks = 5) +
    theme(legend.position="none") 
  ) %>% 
  plot_grid(plotlist = .
            , ncol = 1
            , labels = "auto"
              ) 

plot <- list(legend, fig) %>% 
   plot_grid(plotlist = .
            , ncol = 1
            , rel_heights = c(0.05, 1)
              ) 

plot %>% 
  ggsave2(plot = .
          , "files/Fig-2.jpg"
          , units = "cm"
          , width = 12
          , height = 24
         )

plot %>% 
  ggsave2(plot = .
          , "files/Fig-2.eps"
          , units = "cm"
          , width = 12
          , height = 24
         )

include_graphics("files/Fig-2.jpg")

6.3.13 Table

tab <- 5:length(rsl) %>% map(\(x) { 
  
  trait <- names(rsl[[x]]$mc)[[3]]
  
  rsl[[x]]$mc %>% 
    mutate(across(where(is.numeric), ~ round(., 2))) %>% 
    unite({{trait}}, c({{trait}}, group), sep = " ") %>% 
    select(1:3)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .) %>% 
  rename(Variety = "variedad" 
         , Treatment = trat
         , "Plant height (cm)" = "alt_planta"
         , "Stem thickness (mm)" = "gsr_tallo"
         , "Leaves number" = "nhp_hoja"
         , "Leaf length (cm)" = "larg_hoja" 
         , "Leaf thickness (mm)" = "grs_hoja"
         , "Leaf width (mm)" = "anch_hoja"
         , "Shoot Dry weight (g)" = "peso_seco_brote"
         )

tab %>% kable()
Variety Treatment Plant height (cm) Stem thickness (mm) Leaves number Leaf length (cm) Leaf thickness (mm) Leaf width (mm) Shoot Dry weight (g)
Creole T0 30.8 a 4.47 a 5.01 a 26.2 a 0.97 a 18.87 a 0.71 a
Creole T1 40.27 b 4.85 a 5.53 a 28.33 b 0.63 b 18.96 a 1.5 b
Creole T2 39.4 b 5.03 a 5.53 a 34.8 a 0.7 a 19.03 b 1.09 a
Creole T3 38.27 a 4.51 a 4.99 a 28.6 a 0.63 a 19.05 a 1.72 a
Creole T4 40.53 a 4.32 a 5.15 a 28.27 a 0.69 a 17.07 a 1.19 a
Creole T5 27.53 a 4.2 a 5 a 23.4 b 0.94 a 18.29 a 0.65 a
Creole T6 24.67 b 4.1 a 5 a 21.53 a 0.81 a 17.55 a 0.55 a
Hybrid T0 26.13 b 3.89 b 4.67 b 22.67 b 0.88 a 16.61 b 0.43 a
Hybrid T1 46.33 a 4.92 a 5.47 a 36.27 a 0.84 a 20.04 a 2.03 a
Hybrid T2 44.2 a 5.38 a 5.53 a 33.87 a 0.76 a 21.77 a 1.21 a
Hybrid T3 37.07 a 4.02 b 5.01 a 28.93 a 0.69 a 17.52 a 1.41 b
Hybrid T4 33.07 b 3.7 b 5.01 a 23.33 b 0.64 a 16.07 a 1.1 a
Hybrid T5 30.27 a 4.07 a 5.01 a 26.47 a 0.81 b 19.52 a 0.64 a
Hybrid T6 28.8 a 4.22 a 5 a 23.07 a 0.72 a 17.86 a 0.46 a

tab %>% sheet_write(data = ., gs, "table")

6.3.14 PCA

blues <- 1:length(rsl) %>% map(\(x) { 
  
  rsl[[x]]$mc %>% 
    select(1:3)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .) %>% 
    rename(Variety = "variedad" 
         , Treatment = trat
         , "Plant height (cm)" = "alt_planta"
         , "Stem thickness (mm)" = "gsr_tallo"
         , "Leaves number" = "nhp_hoja"
         , "Leaf length (cm)" = "larg_hoja" 
         , "Leaf thickness (mm)" = "grs_hoja"
         , "Leaf width (mm)" = "anch_hoja"
         , "Shoot Dry weight (g)" = "peso_seco_brote"
         #>
         , "Root length (cm)" = "raiz_lgtd"
         , "Root thickness (mm)" = "gsr_raiz"
         , "Root number" = "num_raiz"
         , "Root Dry weight (g)" = peso_seco_raiz
         )
  
blues %>% str()
## 'data.frame':    14 obs. of  13 variables:
##  $ Variety             : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 2 2 2 ...
##  $ Treatment           : Factor w/ 7 levels "T0","T1","T2",..: 1 2 3 4 5 6 7 1 2 3 ...
##  $ Root length (cm)    : num  11 14 13.3 12.9 15.1 ...
##  $ Root thickness (mm) : num  1.256 0.749 0.926 0.86 0.985 ...
##  $ Root number         : num  10.9 14.5 15.1 10.7 13.4 ...
##  $ Root Dry weight (g) : num  0.635 1.214 0.813 1.057 0.843 ...
##  $ Plant height (cm)   : num  30.8 40.3 39.4 38.3 40.5 ...
##  $ Stem thickness (mm) : num  4.47 4.85 5.03 4.51 4.32 ...
##  $ Leaves number       : num  5.01 5.53 5.53 4.99 5.15 ...
##  $ Leaf length (cm)    : num  26.2 28.3 34.8 28.6 28.3 ...
##  $ Leaf thickness (mm) : num  0.97 0.635 0.701 0.63 0.685 ...
##  $ Leaf width (mm)     : num  18.9 19 19 19.1 17.1 ...
##  $ Shoot Dry weight (g): num  0.71 1.5 1.09 1.72 1.19 ...
pca <- blues %>% 
  select(!c("Leaves number")) %>% 
  unite("treat", c(Treatment, Variety), remove = F, sep = "-") %>% 
  column_to_rownames("treat") %>% 
  PCA(scale.unit = T, quali.sup = c(1:2), graph = F)

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = c(1:2), graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               5.26   2.29   0.98   0.58   0.34   0.24   0.19   0.06
## % of var.             52.62  22.93   9.78   5.75   3.44   2.40   1.90   0.63
## Cumulative % of var.  52.62  75.55  85.33  91.09  94.53  96.93  98.83  99.46
##                       Dim.9 Dim.10
## Variance               0.03   0.02
## % of var.              0.33   0.21
## Cumulative % of var.  99.79 100.00
## 
## Individuals
##                         Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## T0-Creole            |  4.11 | -1.80  4.39  0.19 |  2.83 25.00  0.48 | -2.05
## T1-Creole            |  3.21 |  2.73 10.15  0.73 | -1.24  4.77  0.15 |  0.45
## T2-Creole            |  2.92 |  2.18  6.45  0.56 |  1.03  3.29  0.12 |  0.62
## T3-Creole            |  2.55 |  1.55  3.26  0.37 | -0.93  2.68  0.13 | -1.33
## T4-Creole            |  1.93 |  0.33  0.15  0.03 | -0.68  1.44  0.12 |  1.22
## T5-Creole            |  3.41 | -2.87 11.15  0.71 |  1.25  4.88  0.13 |  1.14
## T6-Creole            |  2.81 | -2.57  9.00  0.84 | -0.45  0.64  0.03 |  0.00
## T0-Hybrid            |  3.66 | -3.42 15.88  0.87 | -0.03  0.00  0.00 |  1.23
## T1-Hybrid            |  4.47 |  3.94 21.11  0.78 |  0.97  2.95  0.05 |  0.98
## T2-Hybrid            |  3.74 |  2.46  8.24  0.44 |  2.23 15.45  0.36 | -0.13
## T3-Hybrid            |  2.87 |  1.38  2.60  0.23 | -1.96 11.91  0.47 | -1.16
## T4-Hybrid            |  3.07 | -0.95  1.22  0.10 | -2.75 23.47  0.80 | -0.50
## T5-Hybrid            |  1.94 | -1.10  1.65  0.32 |  0.60  1.11  0.09 | -0.53
## T6-Hybrid            |  2.42 | -1.87  4.76  0.60 | -0.88  2.41  0.13 |  0.06
##                        ctr  cos2  
## T0-Creole            30.57  0.25 |
## T1-Creole             1.48  0.02 |
## T2-Creole             2.77  0.04 |
## T3-Creole            12.86  0.27 |
## T4-Creole            10.79  0.40 |
## T5-Creole             9.48  0.11 |
## T6-Creole             0.00  0.00 |
## T0-Hybrid            11.12  0.11 |
## T1-Hybrid             7.05  0.05 |
## T2-Hybrid             0.13  0.00 |
## T3-Hybrid             9.82  0.16 |
## T4-Hybrid             1.83  0.03 |
## T5-Hybrid             2.08  0.08 |
## T6-Hybrid             0.03  0.00 |
## 
## Variables
##                        Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Root length (cm)     | -0.39  2.82  0.15 | -0.23  2.34  0.05 |  0.84 72.56
## Root thickness (mm)  | -0.63  7.63  0.40 |  0.70 21.21  0.49 |  0.01  0.01
## Root number          |  0.75 10.80  0.57 |  0.10  0.47  0.01 |  0.46 21.30
## Root Dry weight (g)  |  0.68  8.80  0.46 | -0.57 13.96  0.32 | -0.12  1.39
## Plant height (cm)    |  0.96 17.35  0.91 |  0.07  0.23  0.01 |  0.08  0.62
## Stem thickness (mm)  |  0.74 10.38  0.55 |  0.55 13.37  0.31 |  0.11  1.13
## Leaf length (cm)     |  0.90 15.41  0.81 |  0.35  5.27  0.12 |  0.08  0.60
## Leaf thickness (mm)  | -0.51  5.00  0.26 |  0.72 22.60  0.52 |  0.08  0.65
## Leaf width (mm)      |  0.58  6.43  0.34 |  0.67 19.31  0.44 | -0.12  1.50
## Shoot Dry weight (g) |  0.90 15.38  0.81 | -0.17  1.22  0.03 | -0.05  0.25
##                       cos2  
## Root length (cm)      0.71 |
## Root thickness (mm)   0.00 |
## Root number           0.21 |
## Root Dry weight (g)   0.01 |
## Plant height (cm)     0.01 |
## Stem thickness (mm)   0.01 |
## Leaf length (cm)      0.01 |
## Leaf thickness (mm)   0.01 |
## Leaf width (mm)       0.01 |
## Shoot Dry weight (g)  0.00 |
## 
## Supplementary categories
##                         Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## Creole               |  0.41 | -0.06  0.02  -0.10 |  0.26  0.41   0.62 |  0.01
## Hybrid               |  0.41 |  0.06  0.02   0.10 | -0.26  0.41  -0.62 | -0.01
## T0                   |  3.08 | -2.61  0.72  -1.67 |  1.40  0.21   1.36 | -0.41
## T1                   |  3.52 |  3.34  0.90   2.14 | -0.13  0.00  -0.13 |  0.72
## T2                   |  3.02 |  2.32  0.59   1.49 |  1.63  0.29   1.58 |  0.24
## T3                   |  2.44 |  1.47  0.36   0.94 | -1.44  0.35  -1.40 | -1.24
## T4                   |  2.02 | -0.31  0.02  -0.20 | -1.71  0.72  -1.66 |  0.36
## T5                   |  2.36 | -1.98  0.71  -1.27 |  0.92  0.15   0.90 |  0.30
## T6                   |  2.53 | -2.22  0.77  -1.43 | -0.67  0.07  -0.65 |  0.03
##                       cos2 v.test  
## Creole                0.00   0.03 |
## Hybrid                0.00  -0.03 |
## T0                    0.02  -0.60 |
## T1                    0.04   1.07 |
## T2                    0.01   0.36 |
## T3                    0.26  -1.85 |
## T4                    0.03   0.53 |
## T5                    0.02   0.45 |
## T6                    0.00   0.05 |
pcainfo <- factoextra::get_pca_var(pca)
pcainfo$cor
##                           Dim.1       Dim.2        Dim.3       Dim.4
## Root length (cm)     -0.3850142 -0.23160568  0.842557174 -0.23147791
## Root thickness (mm)  -0.6334207  0.69746926  0.009092441  0.15767946
## Root number           0.7538553  0.10399598  0.456517815  0.36200737
## Root Dry weight (g)   0.6804806 -0.56588136 -0.116435572  0.10078095
## Plant height (cm)     0.9555792  0.07280624  0.077966273  0.01295185
## Stem thickness (mm)   0.7388485  0.55381697  0.105138493 -0.26706820
## Leaf length (cm)      0.9003496  0.34780450  0.076577268  0.07725203
## Leaf thickness (mm)  -0.5131467  0.72000262  0.079603893  0.34016346
## Leaf width (mm)       0.5818081  0.66556182 -0.121233647 -0.35752445
## Shoot Dry weight (g)  0.8995921 -0.16715747 -0.049475693  0.18589699
##                             Dim.5
## Root length (cm)      0.156691674
## Root thickness (mm)   0.022040900
## Root number          -0.142026846
## Root Dry weight (g)   0.373757353
## Plant height (cm)    -0.081352886
## Stem thickness (mm)   0.005369376
## Leaf length (cm)     -0.125316855
## Leaf thickness (mm)   0.276041379
## Leaf width (mm)       0.184068744
## Shoot Dry weight (g)  0.164719886
pcainfo$contrib
##                          Dim.1      Dim.2        Dim.3       Dim.4        Dim.5
## Root length (cm)      2.817341  2.3388953 72.555264317  9.31529745  7.127583908
## Root thickness (mm)   7.625538 21.2110668  0.008449503  4.32242925  0.141029240
## Root number          10.800951  0.4715693 21.300295456 22.78308086  5.855869417
## Root Dry weight (g)   8.800706 13.9624989  1.385611659  1.76577167 40.553653983
## Plant height (cm)    17.354792  0.2311264  0.621274762  0.02916359  1.921305491
## Stem thickness (mm)  10.375209 13.3734939  1.129779427 12.40000834  0.008369473
## Leaf length (cm)     15.406653  5.2745177  0.599335367  1.03752237  4.558998802
## Leaf thickness (mm)   5.004596 22.6037514  0.647647639 20.11652502 22.120700640
## Leaf width (mm)       6.433473 19.3147533  1.502161088 22.22230592  9.835820578
## Shoot Dry weight (g) 15.380741  1.2183272  0.250180782  6.00789552  7.876668468

6.3.15 Figure 3

var <- pca %>% 
  plot.PCA(choix = "var"
           , cex = 0.7 
           )

ind <- pca %>% 
  plot.PCA(choix = "ind", habillage = 2
           , label = c("ind")
           , invisible = "quali"
           ) +
  labs(colour = "Treatments") +
  theme(legend.position = "bottom"
        , legend.direction = "horizontal") +
  guides(colour = guide_legend(nrow = 1))
  

fig <- list(var, ind) %>% 
    plot_grid(plotlist = .
              , ncol = 2
              , labels = "auto"
              , rel_widths = c(1.5, 2)
              ) 

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.jpg"
          , units = "cm"
          , width = 30
          , height = 12
         )

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.eps"
          , units = "cm"
          , width = 30
          , height = 12
         )

include_graphics("files/Fig-3.jpg")